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All Journal International Conference on Sciences Development and Technology
Anjas Fernando
Universitas Islam Negeri Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Gold Price Prediction Using the Fuzzy Time Series Saxena-Easo Method Anjas Fernando; Riri Syafitri Lubis; Hendra Cipta
International Conference on Sciences Development and Technology The 1st ICoSDTech 2021
Publisher : International Conference on Sciences Development and Technology

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Abstract

Gold is an investment that has the smallest risk compared to other investment methods, but every time the price of gold always fluctuates. This fluctuation will make it difficult for investors, the government, or those who need gold price data to see how the prospects for gold investment are going forward. To overcome this, a prediction or forecast is made. One of the forecasting methods developed with fuzzy theory is the Saxena-Easo fuzzy time series method. In this study, this method will be used to predict gold prices in the period 17 August 2021 to 31 December 2021. The purpose of this study is to determine the value of gold prices for the period 17 August 2021 to 31 December 2021 and to determine the accuracy of the fuzzy time series method. Saxena Easo. While the parameter used to measure forecasting accuracy is MAPE, if this parameter is in the range of 10% to 20% then the forecasting result is good, and if the parameter is below 10% then the forecast result is very good. The results of forecasting gold prices in the period 17 August – 31 December 2021 have a value that tends to increase and the MAPE obtained is 0.024277%. Therefore, it can be said that the prediction results for certain parameters are very good.
Co-Authors Hendra Cipta Lubis, Riri Syafitri